CODEWITHSUNDEEP
r
ale19
ale19

Reputation: 1367

Delete rows where any column contains number

This is almost certainly a duplicate question, but I can't find an an answer anywhere on SO. Most of the other similar questions relate to subsetting from one column, rather than an entire dataframe.

I have a dataframe:

test = data.frame(
'A' = c(.31562, .48845, .27828, -999),
'B' = c(.5674, 5.7892, .4687, .1345),
'C' = c(-999, .3145, .0641, -999))

I want to drop rows where any column contains -999, so that my dataframe will look like this:

           A      B         C
2    0.48845 5.7892    0.3145
3    0.27828 0.4687    0.0641

I am sure there is an easy way to do this with the subset() function, or apply(), but I just can't figure it out.

I tried this:

test[apply(test, MARGIN = 1, FUN = function(x) {-999 != x}), ]

But it returns:

              A      B         C
1       0.31562 0.5674 -999.0000
2       0.48845 5.7892    0.3145
4    -999.00000 0.1345 -999.0000
NA           NA     NA        NA
NA.1         NA     NA        NA
NA.2         NA     NA        NA
NA.3         NA     NA        NA
NA.4         NA     NA        NA
NA.5         NA     NA        NA

Upvotes: 1

Views: 249

Answers (2)

akrun
akrun

Reputation: 887213

We can use Reduce

test[!Reduce(`|`, lapply(test, `==`, -999)),]
#        A      B      C
#2 0.48845 5.7892 0.3145
#3 0.27828 0.4687 0.0641

Upvotes: 1

d.b
d.b

Reputation: 32548

Use arr.ind with which to obtain the rows where -999 is present (which(test == -999, arr.ind = TRUE)[,1])and remove those rows.

test[-unique(which(test == -999, arr.ind = TRUE)[,1]),]
#        A      B      C
#2 0.48845 5.7892 0.3145
#3 0.27828 0.4687 0.0641

Upvotes: 3

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